{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d8eefcaf",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Mplfinance Used To Plot Golden Cross Over\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ce6cc6",
   "metadata": {},
   "source": [
    "### What Is a Golden Cross?\n",
    "A golden cross is a chart pattern in which a relatively short-term moving average crosses above a long-term moving average.\n",
    "- The golden cross is a bullish breakout pattern formed from a crossover involving a security's short-term moving average (such as the 15-day moving average) breaking above its long-term moving average (such as the 50-day moving average) or resistance level. \n",
    "- As long-term indicators carry more weight, the golden cross indicates a bull market on the horizon and is reinforced by high trading volumes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c97f5ea0",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### mplfinance 'yahoo' styles was used to customize:\n",
    "- Type of Plot Use `candle`\n",
    "- Dochian Channel Build With Three Types Lines Named Upper Band, Middle Band and Lower Band\n",
    "- Background, Grid, and Figure Colors\n",
    "- Grid style\n",
    "- Y-Axis On The Right or Left\n",
    "- Matplotlib Defaults\n",
    "- Alpha\n",
    "- Color\n",
    "- Markers\n",
    "#### The simplest way to do this is to choose one of the `add_plot` that come packaged with `mplfinance`\n",
    "#### but, as we see below, it is also possible to customize your own `mplfinance styles`.\n",
    "#### Also Other Plot Type Can Be Used\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9eda4d9",
   "metadata": {},
   "source": [
    "import pandas as pd\n",
    "import mplfinance as mpf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c532b75f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e14e3717",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import mplfinance as mpf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32a433d",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a410f0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-06-25</th>\n",
       "      <td>20.530001</td>\n",
       "      <td>20.83</td>\n",
       "      <td>19.99</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>13.693501</td>\n",
       "      <td>61250600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-26</th>\n",
       "      <td>20.299999</td>\n",
       "      <td>20.76</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.629999</td>\n",
       "      <td>14.096654</td>\n",
       "      <td>52904900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High    Low      Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2003-06-25  20.530001  20.83  19.99  20.040001  13.693501  61250600\n",
       "2003-06-26  20.299999  20.76  20.15  20.629999  14.096654  52904900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-07</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.129999</td>\n",
       "      <td>27.480000</td>\n",
       "      <td>27.620001</td>\n",
       "      <td>18.923342</td>\n",
       "      <td>72680200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-08</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.139999</td>\n",
       "      <td>27.200001</td>\n",
       "      <td>27.370001</td>\n",
       "      <td>18.752058</td>\n",
       "      <td>71791400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2004-04-07  28.08  28.129999  27.480000  27.620001  18.923342  72680200\n",
       "2004-04-08  28.08  28.139999  27.200001  27.370001  18.752058  71791400"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.read_csv('../data/yahoofinance-INTC-19950101-20040412.csv',index_col=0,parse_dates=True).tail(200)\n",
    "\n",
    "df = idf.copy()\n",
    "df.index.name = 'Date'\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c79c2d",
   "metadata": {},
   "source": [
    "---\n",
    "- **Golden Crossover has 2 lines: Short Term Moving and Long Term Moving Average. They are calculated as follows:**\n",
    "\n",
    "- Short Term = The Rolling Mean the Previous Short periods\n",
    "- Long Term = The Rolling Mean the Previous Long periods\n",
    "- **Here is Following Calculation:**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a4e807cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['SMA21'] = df['Close'].rolling(window=21).mean()\n",
    "df['SMA50'] = df['Close'].rolling(window=50).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287f1b1c",
   "metadata": {},
   "source": [
    "### Function For Color Coding Golden Crossover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e3a3f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def color(goldencrossover):\n",
    "    UP = []\n",
    "    DOWN = []\n",
    "    for i in range(len(goldencrossover)):\n",
    "        if goldencrossover['SMA50'][i] < goldencrossover['SMA21'][i]:\n",
    "            UP.append(float(goldencrossover['SMA50'][i]))\n",
    "            DOWN.append(np.nan)\n",
    "        elif goldencrossover['SMA50'][i] > goldencrossover['SMA21'][i]:\n",
    "            DOWN.append(float(goldencrossover['SMA50'][i]))\n",
    "            UP.append(np.nan)\n",
    "        else:\n",
    "            UP.append(np.nan)\n",
    "            DOWN.append(np.nan)\n",
    "    goldencrossover['up'] = UP\n",
    "    goldencrossover['down'] = DOWN\n",
    "    return goldencrossover"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0312ef",
   "metadata": {},
   "source": [
    "### Function For Checking Cross Over Golden Crossover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e73a94e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def golden_cal(df):\n",
    "    goldenSignal = []\n",
    "    deathSignal = []\n",
    "    position = False\n",
    "    for i in range(len(df)):\n",
    "        if df['SMA21'][i] > df['SMA50'][i]:\n",
    "            if position == False :\n",
    "                goldenSignal.append((df['SMA50'][i]-df['SMA50'][i]*0.01))\n",
    "                deathSignal.append(np.nan)\n",
    "                position = True\n",
    "            else:\n",
    "                goldenSignal.append(np.nan)\n",
    "                deathSignal.append(np.nan)\n",
    "        elif df['SMA21'][i] < df['SMA50'][i]:\n",
    "            if position == True:\n",
    "                goldenSignal.append(np.nan)\n",
    "                deathSignal.append((df['SMA50'][i]+df['SMA50'][i]*0.01))\n",
    "                position = False\n",
    "            else:\n",
    "                goldenSignal.append(np.nan)\n",
    "                deathSignal.append(np.nan)\n",
    "        else:\n",
    "            goldenSignal.append(np.nan)\n",
    "            deathSignal.append(np.nan)\n",
    "    df['GoldenCrossOver'] = goldenSignal\n",
    "    df['DeathCrossOver'] = deathSignal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a9418bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "golden_cal(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0043a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fuction Color Applied And Df Generated \n",
    "goldencrossover = color(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84416a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Extracted And New Variable Applied\n",
    "up_sma100 = goldencrossover[['up']]\n",
    "down_sma100 = goldencrossover[['down']]\n",
    "up_sma21 = goldencrossover[['SMA21']]\n",
    "dco = goldencrossover[['GoldenCrossOver']]\n",
    "gco = goldencrossover[['DeathCrossOver']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54195a6c",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Let's say we want to plot the Ichimoku Cloud along with the basic OHLCV plot.  \n",
    "\n",
    "We Use `make_addplot()` to create the addplot dict, and pass that into the plot() function:\n",
    "\n",
    "We Use `Color` To Define Line Colors\n",
    "\n",
    "We Use `alpha` To Define Depth Line Color\n",
    "\n",
    "We Use `Makers` To Define Cross Over Point\n",
    "\n",
    "We Use `linestyle` to Highligh Short Term Moving Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3cc8fd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ic = [\n",
    "    #Golden Crossover\n",
    "    mpf.make_addplot(up_sma100,color = 'green',panel=0,),\n",
    "    mpf.make_addplot(down_sma100,color = '#FF8849',panel=0,),\n",
    "    mpf.make_addplot(up_sma21,color = '#0496ff',panel=0,linestyle='dashdot'),\n",
    "    mpf.make_addplot(gco,type='scatter',markersize=200,marker='v',color='red',panel=0),\n",
    "    mpf.make_addplot(dco,type='scatter',markersize=200,marker='^',color='green',panel=0),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "236c94af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(\n",
    "    df,\n",
    "    volume=True,\n",
    "    type=\"candle\", \n",
    "    style=\"yahoo\",\n",
    "    addplot=ic,\n",
    "    figsize=(20,10)\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
